






"""simple docstring"""





import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

  if is_vision_available():
    from transformers import OneFormerImageProcessor
    from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
    from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
  from PIL import Image







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Any="shi-labs/oneformer_demo"					) ->							Optional[int]:
  with open(hf_hub_download(_lowerCamelCase						,							_lowerCamelCase						,							repo_type='dataset'					)						,							'r'					) as f:
    lowerCamelCase_       	=		json.load(_lowerCamelCase					)
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		[]
  lowerCamelCase_       	=		[]
  for key, info in class_info.items():
    lowerCamelCase_       	=		info['name']
    class_names.append(info['name']					)
    if info["isthing"]:
      thing_ids.append(int(_lowerCamelCase					)					)
  lowerCamelCase_       	=		thing_ids
  lowerCamelCase_       	=		class_names
  return metadata






class 	a		(   unittest.TestCase						):






    def __init__(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				List[Any]=7			,		__SCREAMING_SNAKE_CASE	:				List[Any]=3			,		__SCREAMING_SNAKE_CASE	:				List[Any]=30			,		__SCREAMING_SNAKE_CASE	:				Tuple=400			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=None			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				int=True			,		__SCREAMING_SNAKE_CASE	:				Any=[0.5, 0.5, 0.5]			,		__SCREAMING_SNAKE_CASE	:				str=[0.5, 0.5, 0.5]			,		__SCREAMING_SNAKE_CASE	:				int=10			,		__SCREAMING_SNAKE_CASE	:				List[str]=False			,		__SCREAMING_SNAKE_CASE	:				Any=255			,		__SCREAMING_SNAKE_CASE	:				List[Any]="shi-labs/oneformer_demo"			,		__SCREAMING_SNAKE_CASE	:				Any="ade20k_panoptic.json"			,		__SCREAMING_SNAKE_CASE	:				Dict=10			,		)	->						Dict:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		min_resolution
      lowerCamelCase_       	=		max_resolution
      lowerCamelCase_       	=		do_resize
      lowerCamelCase_       	=		{'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
      lowerCamelCase_       	=		do_normalize
      lowerCamelCase_       	=		image_mean
      lowerCamelCase_       	=		image_std
      lowerCamelCase_       	=		class_info_file
      lowerCamelCase_       	=		prepare_metadata(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		num_text
      lowerCamelCase_       	=		repo_path

      # for the post_process_functions
      lowerCamelCase_       	=		2
      lowerCamelCase_       	=		10
      lowerCamelCase_       	=		10
      lowerCamelCase_       	=		3
      lowerCamelCase_       	=		4
      lowerCamelCase_       	=		num_labels
      lowerCamelCase_       	=		do_reduce_labels
      lowerCamelCase_       	=		ignore_index






    def   UpperCamelCase			(			self	:				int   )	->						Optional[int]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "num_labels": self.num_labels,
          "do_reduce_labels": self.do_reduce_labels,
          "ignore_index": self.ignore_index,
          "class_info_file": self.class_info_file,
          "metadata": self.metadata,
          "num_text": self.num_text,
      }






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=False   )	->						Dict:
      if not batched:
        lowerCamelCase_       	=		image_inputs[0]
        if isinstance(__SCREAMING_SNAKE_CASE			,		Image.Image   ):
          lowerCamelCase_	,      lowerCamelCase_       	=		image.size
        else:
          lowerCamelCase_	,      lowerCamelCase_       	=		image.shape[1], image.shape[2]
        if w < h:
          lowerCamelCase_       	=		int(self.size['shortest_edge'] * h / w   )
          lowerCamelCase_       	=		self.size['shortest_edge']
        elif w > h:
          lowerCamelCase_       	=		self.size['shortest_edge']
          lowerCamelCase_       	=		int(self.size['shortest_edge'] * w / h   )
        else:
          lowerCamelCase_       	=		self.size['shortest_edge']
          lowerCamelCase_       	=		self.size['shortest_edge']

      else:
        lowerCamelCase_       	=		[]
        for image in image_inputs:
          lowerCamelCase_	,      lowerCamelCase_       	=		self.get_expected_values([image]   )
          expected_values.append((expected_height, expected_width)   )
        lowerCamelCase_       	=		max(__SCREAMING_SNAKE_CASE			,		key=lambda __SCREAMING_SNAKE_CASE   : item[0]   )[0]
        lowerCamelCase_       	=		max(__SCREAMING_SNAKE_CASE			,		key=lambda __SCREAMING_SNAKE_CASE   : item[1]   )[1]

      return expected_height, expected_width






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      return OneFormerForUniversalSegmentationOutput(
          # +1 for null class
          class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)   )			,		masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)   )			,		)






@require_torch
@require_vision
class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Dict		=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
    # only for test_image_processing_common.test_image_proc_to_json_string
    SCREAMING_SNAKE_CASE		:	Tuple		=					image_processing_class






    def   UpperCamelCase			(			self	:				Any   )	->						Tuple:
      lowerCamelCase_       	=		OneFormerImageProcessorTester(self   )






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						List[Any]:
      return self.image_processing_tester.prepare_image_processor_dict()






    def   UpperCamelCase			(			self	:				Tuple   )	->						Dict:
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'image_mean'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'image_std'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'do_normalize'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'do_resize'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'size'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'ignore_index'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'class_info_file'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'num_text'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'repo_path'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'metadata'   )   )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,		'do_reduce_labels'   )   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      pass






    def   UpperCamelCase			(			self	:				str   )	->						str:
      # Initialize image_processor
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      lowerCamelCase_       	=		prepare_image_inputs(self.image_processing_tester			,		equal_resolution=__SCREAMING_SNAKE_CASE   )
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE			,		Image.Image   )

      # Test not batched input
      lowerCamelCase_       	=		image_processor(image_inputs[0]			,		['semantic']			,		return_tensors='pt'   ).pixel_values

      lowerCamelCase_	,      lowerCamelCase_       	=		self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE   )

      self.assertEqual(
          encoded_images.shape			,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)			,		)

      # Test batched
      lowerCamelCase_	,      lowerCamelCase_       	=		self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE			,		batched=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		image_processor(
          __SCREAMING_SNAKE_CASE			,		['semantic'] * len(__SCREAMING_SNAKE_CASE   )			,		return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape			,		(
              self.image_processing_tester.batch_size,
              self.image_processing_tester.num_channels,
              expected_height,
              expected_width,
          )			,		)






    def   UpperCamelCase			(			self	:				str   )	->						Tuple:
      # Initialize image_processor
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      lowerCamelCase_       	=		prepare_image_inputs(self.image_processing_tester			,		equal_resolution=__SCREAMING_SNAKE_CASE			,		numpify=__SCREAMING_SNAKE_CASE   )
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE			,		np.ndarray   )

      # Test not batched input
      lowerCamelCase_       	=		image_processor(image_inputs[0]			,		['semantic']			,		return_tensors='pt'   ).pixel_values

      lowerCamelCase_	,      lowerCamelCase_       	=		self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE   )

      self.assertEqual(
          encoded_images.shape			,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)			,		)

      # Test batched
      lowerCamelCase_	,      lowerCamelCase_       	=		self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE			,		batched=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		image_processor(
          __SCREAMING_SNAKE_CASE			,		['semantic'] * len(__SCREAMING_SNAKE_CASE   )			,		return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape			,		(
              self.image_processing_tester.batch_size,
              self.image_processing_tester.num_channels,
              expected_height,
              expected_width,
          )			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[str]:
      # Initialize image_processor
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      lowerCamelCase_       	=		prepare_image_inputs(self.image_processing_tester			,		equal_resolution=__SCREAMING_SNAKE_CASE			,		torchify=__SCREAMING_SNAKE_CASE   )
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE			,		torch.Tensor   )

      # Test not batched input
      lowerCamelCase_       	=		image_processor(image_inputs[0]			,		['semantic']			,		return_tensors='pt'   ).pixel_values

      lowerCamelCase_	,      lowerCamelCase_       	=		self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE   )

      self.assertEqual(
          encoded_images.shape			,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)			,		)

      # Test batched
      lowerCamelCase_	,      lowerCamelCase_       	=		self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE			,		batched=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		image_processor(
          __SCREAMING_SNAKE_CASE			,		['semantic'] * len(__SCREAMING_SNAKE_CASE   )			,		return_tensors='pt'   ).pixel_values
      self.assertEqual(
          encoded_images.shape			,		(
              self.image_processing_tester.batch_size,
              self.image_processing_tester.num_channels,
              expected_height,
              expected_width,
          )			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]=False			,		__SCREAMING_SNAKE_CASE	:				Dict=False			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="np"   )	->						Tuple:
      lowerCamelCase_       	=		self.image_processing_class(**self.image_processor_dict   )
      # prepare image and target
      lowerCamelCase_       	=		self.image_processing_tester.num_labels
      lowerCamelCase_       	=		None
      lowerCamelCase_       	=		None
      lowerCamelCase_       	=		prepare_image_inputs(self.image_processing_tester			,		equal_resolution=__SCREAMING_SNAKE_CASE   )
      if with_segmentation_maps:
        lowerCamelCase_       	=		num_labels
        if is_instance_map:
          lowerCamelCase_       	=		list(range(__SCREAMING_SNAKE_CASE   )   ) * 2
          lowerCamelCase_       	=		dict(enumerate(__SCREAMING_SNAKE_CASE   )   )
        lowerCamelCase_       	=		[
            np.random.randint(0			,		high * 2			,		(img.size[1], img.size[0])   ).astype(np.uinta   ) for img in image_inputs
        ]
        if segmentation_type == "pil":
          lowerCamelCase_       	=		[Image.fromarray(__SCREAMING_SNAKE_CASE   ) for annotation in annotations]

      lowerCamelCase_       	=		image_processor(
          __SCREAMING_SNAKE_CASE			,		['semantic'] * len(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE			,		return_tensors='pt'			,		instance_id_to_semantic_id=__SCREAMING_SNAKE_CASE			,		pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE			,		)

      return inputs






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      pass






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Union[str, Any]:
      def common(__SCREAMING_SNAKE_CASE	:				List[Any]=False			,		__SCREAMING_SNAKE_CASE	:				str=None   ):
        lowerCamelCase_       	=		self.comm_get_image_processor_inputs(
            with_segmentation_maps=__SCREAMING_SNAKE_CASE			,		is_instance_map=__SCREAMING_SNAKE_CASE			,		segmentation_type=__SCREAMING_SNAKE_CASE   )

        lowerCamelCase_       	=		inputs['mask_labels']
        lowerCamelCase_       	=		inputs['class_labels']
        lowerCamelCase_       	=		inputs['pixel_values']
        lowerCamelCase_       	=		inputs['text_inputs']

        # check the batch_size
        for mask_label, class_label, text_input in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          self.assertEqual(mask_label.shape[0]			,		class_label.shape[0]   )
          # this ensure padding has happened
          self.assertEqual(mask_label.shape[1:]			,		pixel_values.shape[2:]   )
          self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		self.image_processing_tester.num_text   )

      common()
      common(is_instance_map=__SCREAMING_SNAKE_CASE   )
      common(is_instance_map=__SCREAMING_SNAKE_CASE			,		segmentation_type='pil'   )
      common(is_instance_map=__SCREAMING_SNAKE_CASE			,		segmentation_type='pil'   )






    def   UpperCamelCase			(			self	:				str   )	->						List[Any]:
      lowerCamelCase_       	=		np.zeros((20, 50)   )
      lowerCamelCase_       	=		1
      lowerCamelCase_       	=		1
      lowerCamelCase_       	=		1

      lowerCamelCase_       	=		binary_mask_to_rle(__SCREAMING_SNAKE_CASE   )
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		4   )
      self.assertEqual(rle[0]			,		21   )
      self.assertEqual(rle[1]			,		45   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Any:
      lowerCamelCase_       	=		self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes			,		max_seq_length=77			,		task_seq_length=77			,		class_info_file='ade20k_panoptic.json'			,		num_text=self.image_processing_tester.num_text			,		repo_path='shi-labs/oneformer_demo'			,		)
      lowerCamelCase_       	=		self.image_processing_tester.get_fake_oneformer_outputs()

      lowerCamelCase_       	=		fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE   )

      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		self.image_processing_tester.batch_size   )
      self.assertEqual(
          segmentation[0].shape			,		(
              self.image_processing_tester.height,
              self.image_processing_tester.width,
          )			,		)

      lowerCamelCase_       	=		[(1, 4) for i in range(self.image_processing_tester.batch_size   )]
      lowerCamelCase_       	=		fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE			,		target_sizes=__SCREAMING_SNAKE_CASE   )

      self.assertEqual(segmentation[0].shape			,		target_sizes[0]   )






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes			,		max_seq_length=77			,		task_seq_length=77			,		class_info_file='ade20k_panoptic.json'			,		num_text=self.image_processing_tester.num_text			,		repo_path='shi-labs/oneformer_demo'			,		)
      lowerCamelCase_       	=		self.image_processing_tester.get_fake_oneformer_outputs()
      lowerCamelCase_       	=		image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE			,		threshold=0   )

      self.assertTrue(len(__SCREAMING_SNAKE_CASE   ) == self.image_processing_tester.batch_size   )
      for el in segmentation:
        self.assertTrue('segmentation' in el   )
        self.assertTrue('segments_info' in el   )
        self.assertEqual(type(el['segments_info']   )			,		__SCREAMING_SNAKE_CASE   )
        self.assertEqual(
            el['segmentation'].shape			,		(self.image_processing_tester.height, self.image_processing_tester.width)   )






    def   UpperCamelCase			(			self	:				Any   )	->						Tuple:
      lowerCamelCase_       	=		self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes			,		max_seq_length=77			,		task_seq_length=77			,		class_info_file='ade20k_panoptic.json'			,		num_text=self.image_processing_tester.num_text			,		repo_path='shi-labs/oneformer_demo'			,		)
      lowerCamelCase_       	=		self.image_processing_tester.get_fake_oneformer_outputs()
      lowerCamelCase_       	=		image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE			,		threshold=0   )

      self.assertTrue(len(__SCREAMING_SNAKE_CASE   ) == self.image_processing_tester.batch_size   )
      for el in segmentation:
        self.assertTrue('segmentation' in el   )
        self.assertTrue('segments_info' in el   )
        self.assertEqual(type(el['segments_info']   )			,		__SCREAMING_SNAKE_CASE   )
        self.assertEqual(
            el['segmentation'].shape			,		(self.image_processing_tester.height, self.image_processing_tester.width)   )












"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




